Karbon emisyon politikalarının tersine tedarik zincir ağı tasarımı üzerindeki etkileri

Reverse Supply Chain is described as an initiative that plays an important role in the global supply chain for those who seek environmentally responsible solutions for their end-of-life products. The relative economic and environmental benefits of reverse supply chain are influenced by costs and emissions during collection, transportation, recovery facilities, disassembly, recycling, remanufacturing, and disposal of unrecoverable components. The design of reverse supply chain network takes into account social, economic and environmental objectives. This paper addresses the design of reverse supply chain under the three common regulatory policies, strict carbon caps, carbon tax, and carbon cap-and-trade.Küresel tedarik zincirinde önemli bir rol oynayan tersine tedarik zinciri, ömrünü tamamlamış ürünler için çevreye karşı sorumlu çözümler arayanların bir girişimi olarak tanımlanmaktadır. Tersine tedarik zincirinin nispi ekonomik ve çevresel faydaları, toplama, nakliye, geri kazanım tesisleri, demontaj, geri dönüşüm, yeniden imalat ve geri dönüşü olmayan bileşenlerin imha edilmesi sırasında oluşan maliyetler ve emisyonlardan etkilenmektedir. Tersine tedarik zinciri ağ tasarımı sosyal, ekonomik ve çevresel hedefleri dikkate almaktadır. Bu makale, sıkı karbon kapsülleri, karbon vergisi, karbon emisyon üst sınırı ve ticareti olmak üzere üç ortak düzenleyici politikada ters tedarik zincirinin tasarımını ele almaktadır

The Impact of Carbon Emissions Policies on Reverse Supply Chain Network Design

Reverse Supply Chain is described as an initiative that plays an important role in the global supply chain for those who seek environmentally responsible solutions for their end-of-life products. The relative economic and environmental benefits of reverse supply chain are influenced by costs and emissions during collection, transportation, recovery facilities, disassembly, recycling, remanufacturing, and disposal of unrecoverable components. The design of reverse supply chain network takes into account social, economic and environmental objectives. This paper addresses the design of reverse supply chain under the three common regulatory policies, strict carbon caps, carbon tax, and carbon cap-and-trade

The Impact of Carbon Emissions Policies on Reverse Supply Chain Network Design

Reverse Supply Chain is described as an initiative that plays an important role in the global supply chain for those who seek environmentally responsible solutions for their end-of-life products. The relative economic and environmental benefits of reverse supply chain are influenced by costs and emissions during collection, transportation, recovery facilities, disassembly, recycling, remanufacturing, and disposal of unrecoverable components. The design of reverse supply chain network takes into account social, economic and environmental objectives. This paper addresses the design of reverse supply chain under the three common regulatory policies, strict carbon caps, carbon tax, and carbon cap-and-trade

A demands-matching multi-criteria decision-making method for reverse logistics

A demand matching oriented Multi-Criteria Decision-Making method is presented to identify the best collection mode for used components. In this method, the damage condition and remaining service life are incorporated into the evaluation criteria of reuse mode, then a hybrid method (AHP-EW) integrating Analytic Hierarchy Process (AHP) and Entropy Weight (EW) is used to derive the criteria weights and the grey Multi-Attributive Border Approximation Area Comparison (MABAC) is adopted to rank the collection modes. Finally, a sensitivity analysis is used to test the stability of the method and a demands-matching method is proposed to validate the feasibility of the optimal alternative. The method is validated using the collection of used pressurizers as case study. The results of which show the effectiveness of the proposed method

Tactical supply chain planning under a carbon tax policy scheme: a case study

Greenhouse gas emissions are receiving greater scrutiny in many countries due to international forces to reduce anthropogenic global climate change. Industry and their supply chains represent a major source of these emissions. This paper presents a tactical supply chain planning model that integrates economic and carbon emission objectives under a carbon tax policy scheme. A modified Cross-Entropy solution method is adopted to solve the proposed nonlinear supply chain planning model. Numerical experiments are completed utilizing data from an actual organization in Australia where a carbon tax is in operation. The analyses of the numerical results provide important organizational and policy insights on (1) the financial and emissions reduction impacts of a carbon tax at the tactical planning level, (2) the use of cost/emission tradeoff analysis for making informed decisions on investments, (3) the way to price carbon for maximum environmental returns per dollar increase in supply chain cost

La cadena de suministro sostenible: conceptos, modelos de optimizaci´on y de simulaci´on y tendencias

Context: The environmental and social dimensions of performance are of great importance, given that they must be incorporated into strategic, tactical, and operational objectives in companies and supply chains to minimize negative impacts on the environment and society. Method: After reviewing the Scopus, Web of Science, and ScienceDirect databases for the topics of sustainability and supply chain management, a state of the art of green and sustainable supply chain management is presented, aiming to guide readers towards a synthesis of related concepts and future lines of research. Results: The reader is introduced to concepts and trends around the field of green and sustainable supply chain management to raise interest in new research and practices to guide the implementation of sustainability in organizations and their supply chains. Conclusions: Sustainable supply chain management still faces several academic and practical challenges in terms of implementation, performance measurement, and how models can capture a dynamic and uncertain social and environmental context. There are latent research issues such as management of the circular supply chain, applications in emerging economies, or the application of 4.0 technologies.Contexto: Las dimensiones ambiental y social del desempeño son de gran importancia, puesto que deben ser incorporadas a los objetivos estratégicos, tácticos y operativos de las empresas y cadenas de suministro para minimizar los impactos negativos sobre el medio ambiente y la sociedad. Método: Tras haber consultado los temas de sostenibilidad y gestión de la cadena de suministro en las bases de datos Scopus, Web of Science y ScienceDirect, se presenta el estado del arte en gestión de la cadena de suministro verde y sostenible, esperando guiar a los lectores hacia una síntesis de conceptos relacionados y futuras líneas de investigación. Resultados: Se introduce al lector a conceptos y tendencias en el campo de gestión de la cadena de suministro verde y sostenible para despertar el interés en nuevas investigaciones y prácticas para guiar la implementación de la sostenibilidad en organizaciones y sus cadenas de suministro. Conclusiones: La gestión sostenible de la cadena de suministro aún enfrenta varios retos académicos y prácticos, desafíos en términos de implementación, medición del desempeño y la manera en que los modelos pueden capturar un contexto social y ambiental dinámico e incierto. Hay cuestiones de investigación latentes como la gestión de la cadena de suministro circular, las aplicaciones en economías emergentes o la aplicación de tecnologías 4.0

Sustainability Analysis under Disruption Risks

Resilience to disruptions and sustainability are both of paramount importance to supply chains. This paper presents a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimization model is developed that utilizes a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and buttressing strategies that minimize the expected total cost and maximize the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry

A resilient and sustainable supply chain: Is it affordable?

Developing environmentally and socially sustainable supply chains has become an integral part of corporate strategy for virtually every industry. However, little is understood about the broader impacts of sustainability practices on the capacity of the supply chain to tolerate disruptions. This article aims to investigate the sustainability-resilience relationship at the strategic supply chain design level using a multi-objective optimization model and an empirical case study. The proposed model utilizes a sustainability performance scoring method and a novel programming approach to perform a dynamic sustainability tradeoff analysis and design a “resiliently green” supply chain

A bi-objective optimization model for a carbon cap jit distribution network

The environmental protection concerns and legislation are pushing companies to redesign and plan their activities in an environmental friendly manner. This will probably be done by constraining companies to emit less than a given amount of carbon dioxide per product that is being produced and transported. In addition, some companies may volunteer to reduce their carbon footprint. Consequently, companies will face new constraints that force them to reduce carbon emissions while still minimizing production and transportation costs. Transportation is at the heart of logistics activities and is one of the leading sources of greenhouse gas emissions. The emitted carbon dioxide through transportation activities is accounting for almost 80% of the total greenhouse gas emissions. The need to implement Just-In-Time (JIT) strategy for transporting small batch sizes seems to beagainst environmental concerns. The JIT principles favor small and frequent deliveries by many small rush transports with multiple regional warehouses. Although several attempts have been made to analyze green supply chain networks, little attention has been paid to develop JIT distribution models in carbon constrained environment. Incorporation of environmental objectives and constraints with JIT distribution will generate new problems resulting in new combinatorial optimization models. In addition, these objectives and constraints will add to the model complexities. Both areas require to be investigated. In this research, a bi-objective carbon-capped logistic model was developed for a JIT distribution that takes into account different carbon emission constraints. The objectives include minimization of total costs and carbon cap. Since the studied problem is Non-deterministic Polynomial-time Hard (NP-Hard), a nondominated sorting genetic algorithm-II (NSGA-II) was employed to solve the problem. For validation and verification of the obtained results, non-dominated ranking genetic algorithm (NRGA) was applied. Then, Taguchi approach was employed to tune the parameters of both algorithms; their performances were then compared in terms of some multi-objective performance measures. For further improvements of NSGA-II, a modified firefly algorithm as local searcher was applied. Seven problems with different sizes of small, medium, and large were designed in order to simulate the different cases. The findings have significant implications for the understanding of how varying carbon cap could significantly affect total logistics costs and total carbon emission. More specifically, the results also demonstrated devising policies that enable companies to decide when and how to fulfill the required carbon cap could let firms fulfill these caps at significantly lower costs with lower carbon emission. In addition to these findings, the performance of the proposed solution methodology demonstrated higher efficiency particularly in terms of less CPU time usage by 6.62% and higher quality of obtained solutions by 5.14% on average for different sizes of the problem as compared to the classical NSGA-II